2026-06-07T19:59:42Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1001652025-03-18T12:50:39Zcom_20.500.14352_14col_20.500.14352_15

How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters López García, María Eugenia Bruña Fernández, Ricardo Cuesta Prieto, Pablo Marcos Dolado, Alberto Maestu Unturbe, Fernando Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings. 2024-02-08T08:15:19Z 2024-02-08T08:15:19Z 2024-02-08T08:15:19Z 2018-06-01 journal article https://hdl.handle.net/20.500.14352/100165 1662-4548 10.3389/fnins.2018.00306 1662-453X eng MR/K004360/1 FIS2013-41057-P TEC2016-80063-C3-2-R PSI-2015-68793-C3-1-R FPU13/06009 Dimitriadis SI, López ME, Bruña R,Cuesta P, Marcos A, Maestú F andPereda E (2018) How to Build aFunctional Connectomic Biomarker forMild Cognitive Impairment FromSource Reconstructed MEGResting-State Activity: The Combination of ROI Representationand Connectivity Estimator Matters.Front. Neurosci. 12:306 doi:10.3389/fnins.2018.00306 http://creativecommons.org/licenses/by/4.0/ open access Attribution 4.0 International Frontiers